Update license

APACHE_LICENSE_VERSION_2.0.md

@@ -1,201 +0,0 @@
-                                 Apache License
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CC_BY-SA_4.0.md

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Notice

@@ -0,0 +1,2 @@
+Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.
+Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms

README.md

@@ -17,14 +17,22 @@ library_name: fasttext
 
 This repository contains fastText classifiers for judging the educational value of Japanese web pages. It includes two types of classifiers:
 
-1. **Wiki-based classifier**: trained on Japanese Wikipedia text from academic categories. This classifier is released under the [Apache-2.0 License](https://huggingface.co/tokyotech-llm/edu-classifier/blob/main/APACHE_LICENSE_VERSION_2.0.md).
-2. **LLM-based classifier**: trained on annotations provided by an LLM, governed by the license applicable to the underlying LLM used for annotation ([Llama 3.1 Community License Agreement](https://huggingface.co/tokyotech-llm/edu-classifier/blob/main/LLAMA_3) or [Gemma Terms of Use](https://huggingface.co/tokyotech-llm/edu-classifier/blob/main/GEMMA_TERMS_OF_USE.md)).
+1. **Wiki-based classifier**: trained on Japanese Wikipedia text in academic categories.
+2. **LLM-based classifier**: trained on annotations generated by LLMs.
 
-These classifiers were developed as part of a quality-filtering process for Swallow Corpus Version 2, used in the training of the [Llama 3.1 Swallow](https://huggingface.co/collections/tokyotech-llm/llama-31-swallow-66fd4f7da32705cadd1d5bc6) series. Our ablation experiments have shown that applying a filter based on the classifier’s scores improved the LLM’s ability related to Japanese knowledge.
+The Wiki-based classifier is distributed under the [CC BY-SA 4.0](https://huggingface.co/tokyotech-llm/edu-classifier/blob/main/CC_BY-SA_4.0.md) license, while the LLM-based classifier is distributed under the license of the LLM used for annotation ([Llama 3.1 Community License Agreement](https://huggingface.co/tokyotech-llm/edu-classifier/blob/main/LLAMA_3.1_COMMUNITY_LICENSE_AGREEMENT.md) or [Gemma Terms of Use](https://huggingface.co/tokyotech-llm/edu-classifier/blob/main/GEMMA_TERMS_OF_USE.md)).
+
+These classifiers were developed as part of a quality-filtering process for the \*Swallow Corpus Version 2, which was used to train the [Llama 3.1 Swallow](https://huggingface.co/collections/tokyotech-llm/llama-31-swallow-66fd4f7da32705cadd1d5bc6) series. Our ablation experiments have shown that applying a filter based on the classifier’s scores improved the LLM’s ability related to Japanese knowledge.
+
+\* A large Japanese web corpus extracted from Common Crawl
 
 ### How to use
 
-The Wiki-based classifier outputs a probability between 0 and 1, indicating how likely a given document resembles Wikipedia content. On the other hand, the LLM-based classifier predicts which of the four labels (0, 1, 2, or 3) the educational score of a given document belongs to, treating it as a four-class classification problem. The expected value of the score (ranging from 0 to 3), based on the predicted probabilities for each label, can be considered the final score.
+The Wiki-based classifier outputs a probability between 0 and 1, indicating how likely a given document resembles Wikipedia content. On the other hand, the LLM-based classifier predicts which of the four labels (0, 1, 2, or 3) the educational score of a given document belongs to, treating it as a 4-class classification problem. The expected value of the score (ranging from 0 to 3), based on the predicted probabilities for each label, can be considered the final score.
+
+```bash
+pip install numpy==1.26.4 fasttext
+```
 
 ```python
 from huggingface_hub import hf_hub_download
@@ -53,9 +61,31 @@ edu_score = sum([int(label[-1]) * prob for label, prob in zip(res[0], res[1])])
 
 ### Best practice
 
-If you aim to assign appropriate ranked scores to a wide range of documents, it is recommended to use the LLM-based classifier. The Wiki-based classifier tends to assign scores close to 0 for most documents, making it specialized for detecting the few documents that resemble Wikipedia. In contrast, the LLM-based classifier can provide grading based on a broader definition of educational value.
+In our research, we have demonstrated that both classifiers are effective. However, we recommend using the **LLM-based classifier** if you want to assign appropriate ranking scores to a broader range of documents. The Wiki-based classifier is specialized in detecting certain useful documents and tends to assign scores close to 0 for most documents. In contrast, the LLM-based classifier provides scoring based on a more general definition of educational value.
+
+## Training
+
+Both classifiers were trained using fastText with 20 epochs on the training data. Character n-grams $(n=2,3)$ were used as features. Word n-grams were not applied as they did not contribute to improve accuracy.
+
+### Wiki-based Classifier
+
+We built this classifier by treating Wikipedia articles as positive examples of educational documents. Since not all articles, such as those about individuals, are necessarily “educational”, we extracted 37,999 Japanese Wikipedia articles from academic categories as positive examples for training data. For the negative examples, we randomly sampled 37,999 documents from the Swallow Corpus Version 2.
+
+### LLM-based Classifier
+
+Inspired by [FineWeb-Edu](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier), we constructed the classifier through the following steps:
+
+1. Use 200,000 documents randomly extracted from the Swallow Corpus Version 2 and an additional 31,059 web articles that were manually selected.
+2. Evaluate the educational value of the documents from step 1 using [Llama 3.1 70B Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) (or [Gemma 2 27B IT](https://huggingface.co/google/gemma-2-27b-it)) and a [custom prompt](https://huggingface.co/tokyotech-llm/edu-classifier/blob/main/utils/prompt.md). The evaluation is based on three criteria: (1) Whether the topic is highly academic. (2) Whether it provides deep insights or discussions. (3) Whether it is easy to understand for a general audience. The educational value is scored on a 3-point scale.
+3. Train a fastText classifier using the automatically scored documents from step 2 as training data. The classifier predicts the educational score as one of four classes (0, 1, 2, or 3).
+
+## Acknowledgments
+
+This research is based on results obtained from a project, JPNP18002, commissioned by the New Energy and Industrial Technology Development Organization (NEDO). In addition, the experiments of continual pre-training of LLMs was supported by the “Support Program for Building Large Language Models” of the AI Bridging Cloud Infrastructure (ABCI) developed and operated by the National Institute of Advanced Industrial Science and Technology (AIST). This research is based on results obtained from AIST policy-based budget project "R&D on Generative AI Foundation Models for the Physical Domain".
+
+## Citation
 
-## How to cite
+(Japanese only)
 
 ```bibtex
 @inproceedings{hattori-2025-swallow-v2,

README_ja.md

@@ -4,14 +4,20 @@
 
 **注意**：日本語でのみ動作します。英語やそれ以外の言語での品質は保証しません。
 
-日本語ウェブページの教育的価値を判定する fastText 分類器です。本リポジトリには学術カテゴリに属する日本語 Wikipedia テキストを元に訓練された分類器（Wiki-based classifier）と、LLM によるアノテーションを元に訓練された分類器（LLM-based classifier）が含まれます。前者には [Apache-2.0 ライセンス](https://huggingface.co/tokyotech-llm/edu-classifier/blob/main/APACHE_LICENSE_VERSION_2.0.md)、後者にはアノテーションに使用された LLM に応じたライセンス（[Llama 3.1 Community License Agreement](https://huggingface.co/tokyotech-llm/edu-classifier/blob/main/LLAMA_3.1_COMMUNITY_LICENSE_AGREEMENT.md)、[Gemma Terms of Use](https://huggingface.co/tokyotech-llm/edu-classifier/blob/main/GEMMA_TERMS_OF_USE.md)）が適用されます。
+日本語ウェブページの教育的価値を判定する fastText 分類器です。本リポジトリには学術カテゴリに属する日本語 Wikipedia テキストを元に訓練された分類器（**Wiki-based classifier**）と、LLM によるアノテーションを元に訓練された分類器（**LLM-based classifier**）が含まれます。前者には[CC BY-SA 4.0](https://huggingface.co/tokyotech-llm/edu-classifier/blob/main/CC_BY-SA_4.0.md)、後者にはアノテーションに使用された LLM に応じたライセンス（[Llama 3.1 Community License Agreement](https://huggingface.co/tokyotech-llm/edu-classifier/blob/main/LLAMA_3.1_COMMUNITY_LICENSE_AGREEMENT.md)、[Gemma Terms of Use](https://huggingface.co/tokyotech-llm/edu-classifier/blob/main/GEMMA_TERMS_OF_USE.md)）が適用されます。
 
-これらの分類器は[Llama 3.1 Swallow](https://huggingface.co/collections/tokyotech-llm/llama-31-swallow-66fd4f7da32705cadd1d5bc6)シリーズの訓練に用いられた Swallow Corpus Version 2 の品質フィルタリングの一環として開発されました。Ablation 実験では、分類器のスコアに基づくフィルタリングの適用により、LLM の日本語知識が向上することを確認しました。
+これらの分類器は、[Llama 3.1 Swallow](https://huggingface.co/collections/tokyotech-llm/llama-31-swallow-66fd4f7da32705cadd1d5bc6)シリーズの訓練に用いられた \*Swallow コーパス v2 の品質フィルタリングの一環として開発されました。Ablation 実験では、分類器のスコアに基づくフィルタリングの適用により、同等の計算規模の学習で、より効果的に LLM の日本語知識が向上することを確認しました。
+
+\* Common Crawl 由来の大規模日本語コーパス
 
 ### 使用法
 
 Wiki-based classifier は与えられた文書が Wikipedia らしいかどうかを 0〜1 の確率で出力します。一方、LLM-based classifier は与えられた文書の教育的スコアが 0、1、2、3 のいずれに属するかどうかを 4 ラベル分類問題として予測します。各ラベルの予測確率に基づくスコアの期待値（0〜3）を最終的なスコアとして用いることができます。
 
+```bash
+pip install numpy==1.26.4 fasttext
+```
+
 ```python
 from huggingface_hub import hf_hub_download
 import fasttext
@@ -39,10 +45,32 @@ edu_score = sum([int(label[-1]) * prob for label, prob in zip(res[0], res[1])])
 
 ### ベストプラクティス
 
-広範な文書に適切な序列のスコアを付与したい場合、LLM-based classifier の使用を推奨します。Wiki-based classifier はほとんどの文書に 0 付近のスコアを付与する傾向にあるため、Wikipedia らしいわずかな文書の検出に特化しています。一方、LLM-based classifier はより一般的な教育的価値の定義に基づいた採点をすることができます。
+研究では、どちらの分類器も有効であることを確認していますが、より広範な文書に適切な序列のスコアを付与したい場合には、LLM-based classifier の使用を推奨します。Wiki-based classifier は一部の有用な文書の検出に特化しており、ほとんどの文書には 0 付近のスコアを付与する傾向にあります。一方、LLM-based classifier はより一般的な教育的価値の定義に基づいた採点をすることができます。
+
+## 訓練
+
+いずれの分類器も、fastText で訓練データを 20 エポック学習しました。文字 n-gram $(n=2,3)$を特徴量に用いました。単語 n-gram は精度向上に繋がらなかったため、採用しませんでした。
+
+### Wiki-based classifier
+
+Wikipedia 記事を教育的な文書の正例と見なし、分類器を構築しました。人物に関する記事など、必ずしも教養的とは言えない記事もあるため、学術分野のカテゴリに属する日本語 Wikipedia 記事 37,999 件を抽出し、訓練データの正例と��ました。また、負例は Swallow コーパス v2 からランダムにサンプリングした文書 37,999 件としました。
+
+### LLM-based classifier
+
+[FineWeb-Edu](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier)を参考に、以下の手順で構築しました。
+
+1. Swallow Corpus Version 2 からランダムに抽出した 20 万件の文書と、独自に選定したウェブ記事 31,059 件を訓練データとする。
+2. 1.の文書に対して、[Llama 3.1 70B Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct)（もしくは[Gemma 2 27B IT](https://huggingface.co/google/gemma-2-27b-it)）と[プロンプト](https://huggingface.co/tokyotech-llm/edu-classifier/blob/main/utils/prompt.md)を用いて、「高度に学術的なトピックか」「深い洞察や議論を提供しているか」「一般向けに分かりやすいか」の 3 つの基準で文書の教育的価値を採点する（3 点満点）。
+3. 2.で自動採点された文書を訓練データとして、fastText で教育的スコアの点数（0、1、2、3）を予測する 4 クラス分類器を訓練する。
+
+## 謝辞
+
+この成果は、国立研究開発法人新エネルギー・産業技術総合開発機構（NEDO）の委託業務（JPNP18002）の結果得られたものです。また、LLM の継続事前学習の実験では、国立研究開発法人産業技術総合研究所が構築・運用する AI 橋渡しクラウド（ABCI: AI Bridging Cloud Infrastructure）の「大規模言語モデル構築支援プログラム」の支援を受けました。この成果は、産総研政策予算プロジェクト「フィジカル領域の生成 AI 基盤モデルに関する研究開発」の結果得られました。
 
 ## 引用
 
+（日本語のみ）
+
 ```bibtex
 @inproceedings{hattori-2025-swallow-v2,
   author = {服部 翔 and 岡崎 直観 and 水木 栄 and 藤井 一喜 and 中村 泰士 and 大井 聖也 and 塩谷 泰平 and 齋藤 幸史郎 and Youmi Ma and 前田 航希 and 岡本 拓己 and 石田 茂樹 and 横田 理央 and 高村 大也},